A sufficient condition for backtrack-bounded search
Journal of the ACM (JACM)
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Experimental evaluation of preprocessing algorithms for constraint satisfaction problems
Artificial Intelligence
Synthesizing constraint expressions
Communications of the ACM
Domain filtering can degrade intelligent backtracking search
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Selected papers from the Joint ERCIM/Compulog Net Workshop on New Trends in Contraints
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Partition-k-AC: An Efficient Filtering Technique Combining Domain Partition and Arc Consistency
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
A Strong Local Consistency for Constraint Satisfaction
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Bridging the gap between planning and scheduling
The Knowledge Engineering Review
A logical approach to efficient Max-SAT solving
Artificial Intelligence
Domain filtering consistencies for non-binary constraints
Artificial Intelligence
Inverse Consistencies for Non-binary Constraints
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Hybrid tractable CSPs which generalize tree structure
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Domain filtering consistencies
Journal of Artificial Intelligence Research
Local consistency for extended CSPs
Theoretical Computer Science
Reformulating CSPs for scalability with application to geospatial reasoning
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Extracting microstructure in binary constraint networks
CSCLP'06 Proceedings of the constraint solving and contraint logic programming 11th annual ERCIM international conference on Recent advances in constraints
Failed value consistencies for constraint satisfaction
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Integrating strong local consistencies into constraint solvers
CSCLP'09 Proceedings of the 14th Annual ERCIM international conference on Constraint solving and constraint logic programming
A framework for decision-based consistencies
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Algorithms and constraint programming
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
WG'04 Proceedings of the 30th international conference on Graph-Theoretic Concepts in Computer Science
Revisiting neighborhood inverse consistency on binary CSPs
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Maintaining alternative values in constraint-based configuration
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Constraint satisfaction consistency preprocessing methods are used to reduce search effort. Time and especially space costs limit the amount of preprocessing that will be cost effective. A new form of consistency preprocessing, neighborhood inverse consistency, can achieve more problem pruning than the usual arc consistency preprocessing in a cost effective manner. There are two basic ideas: 1) Common forms of consistency enforcement basically operate by identifying and remembering solutions to subproblems for which a consistent value cannot be found for some additional problem variable. The space required for this memory can quickly become prohibitive. Inverse consistency basically operates by removing values for variables that are not consistent with any solution to some subproblem involving additional variables. The space requirement is at worst linear. 2) Typically consistency preprocessing achieves some level of consistency uniformly throughout the problem. A subproblem solution will be tested against each additional variable that constrains any subproblem variable. Neighborhood consistency focuses attention on the subproblem formed by the variables that are all constrained by the value in question. By targeting highly relevant subproblems we hope to "skim the cream", obtaining a high payoff for a limited cost.